Diabetes Prediction¶

Problem Statement¶

The objective of this project is to classify whether someone has diabetes or not. Dataset consists of several Medical Variables(Independent) and one Outcome Variable(Dependent) The outcome variable value is either 1 or 0 indicating whether a person has diabetes(1) or not(0).

In [2]:
import pandas as pd
import numpy as np
import warnings
warnings.filterwarnings('ignore')
In [3]:
df=pd.read_csv('pima_diabetes (1).csv')
In [4]:
df.head()
Out[4]:
Preg Plas Pres skin test mass pedi age class
0 6 148 72 35 0 33.6 0.627 50 1
1 1 85 66 29 0 26.6 0.351 31 0
2 8 183 64 0 0 23.3 0.672 32 1
3 1 89 66 23 94 28.1 0.167 21 0
4 0 137 40 35 168 43.1 2.288 33 1
In [5]:
df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 768 entries, 0 to 767
Data columns (total 9 columns):
 #   Column  Non-Null Count  Dtype  
---  ------  --------------  -----  
 0   Preg    768 non-null    int64  
 1   Plas    768 non-null    int64  
 2   Pres    768 non-null    int64  
 3   skin    768 non-null    int64  
 4   test    768 non-null    int64  
 5   mass    768 non-null    float64
 6   pedi    768 non-null    float64
 7   age     768 non-null    int64  
 8   class   768 non-null    int64  
dtypes: float64(2), int64(7)
memory usage: 54.1 KB
In [6]:
df.shape
Out[6]:
(768, 9)
In [7]:
df.describe().T
Out[7]:
count mean std min 25% 50% 75% max
Preg 768.0 3.845052 3.369578 0.000 1.00000 3.0000 6.00000 17.00
Plas 768.0 120.894531 31.972618 0.000 99.00000 117.0000 140.25000 199.00
Pres 768.0 69.105469 19.355807 0.000 62.00000 72.0000 80.00000 122.00
skin 768.0 20.536458 15.952218 0.000 0.00000 23.0000 32.00000 99.00
test 768.0 79.799479 115.244002 0.000 0.00000 30.5000 127.25000 846.00
mass 768.0 31.992578 7.884160 0.000 27.30000 32.0000 36.60000 67.10
pedi 768.0 0.471876 0.331329 0.078 0.24375 0.3725 0.62625 2.42
age 768.0 33.240885 11.760232 21.000 24.00000 29.0000 41.00000 81.00
class 768.0 0.348958 0.476951 0.000 0.00000 0.0000 1.00000 1.00
In [8]:
df.isna().sum()
Out[8]:
Preg     0
Plas     0
Pres     0
skin     0
test     0
mass     0
pedi     0
age      0
class    0
dtype: int64
In [9]:
df.isna().apply(pd.value_counts).T
Out[9]:
False
Preg 768
Plas 768
Pres 768
skin 768
test 768
mass 768
pedi 768
age 768
class 768
In [10]:
import matplotlib.pyplot as plt
import seaborn as sns
import warnings
warnings.filterwarnings('ignore')
In [11]:
sns.displot(df['Preg'])
Out[11]:
<seaborn.axisgrid.FacetGrid at 0x22d9d5b6260>
In [12]:
sns.displot(df['mass'])
Out[12]:
<seaborn.axisgrid.FacetGrid at 0x22d9d5b58d0>
In [13]:
sns.pairplot(df)
Out[13]:
<seaborn.axisgrid.PairGrid at 0x22da0811690>
In [14]:
corr=df.corr()
corr
Out[14]:
Preg Plas Pres skin test mass pedi age class
Preg 1.000000 0.129459 0.141282 -0.081672 -0.073535 0.017683 -0.033523 0.544341 0.221898
Plas 0.129459 1.000000 0.152590 0.057328 0.331357 0.221071 0.137337 0.263514 0.466581
Pres 0.141282 0.152590 1.000000 0.207371 0.088933 0.281805 0.041265 0.239528 0.065068
skin -0.081672 0.057328 0.207371 1.000000 0.436783 0.392573 0.183928 -0.113970 0.074752
test -0.073535 0.331357 0.088933 0.436783 1.000000 0.197859 0.185071 -0.042163 0.130548
mass 0.017683 0.221071 0.281805 0.392573 0.197859 1.000000 0.140647 0.036242 0.292695
pedi -0.033523 0.137337 0.041265 0.183928 0.185071 0.140647 1.000000 0.033561 0.173844
age 0.544341 0.263514 0.239528 -0.113970 -0.042163 0.036242 0.033561 1.000000 0.238356
class 0.221898 0.466581 0.065068 0.074752 0.130548 0.292695 0.173844 0.238356 1.000000
In [15]:
corr['class']
Out[15]:
Preg     0.221898
Plas     0.466581
Pres     0.065068
skin     0.074752
test     0.130548
mass     0.292695
pedi     0.173844
age      0.238356
class    1.000000
Name: class, dtype: float64
In [16]:
plt.figure(figsize=(15,5))
plt.title('Correlation',fontsize=20)
sns.heatmap(corr[['class']],cmap='Blues',vmax=1.0,vmin=-1.0,fmt='g',annot=True)
plt.show()
In [ ]:
In [17]:
import pandas_profiling
import warnings
warnings.filterwarnings('ignore')
In [18]:
pandas_profiling.ProfileReport(df)

Summarize dataset:   0%|          | 0/5 [00:00<?, ?it/s]
Generate report structure:   0%|          | 0/1 [00:00<?, ?it/s]
Render HTML:   0%|          | 0/1 [00:00<?, ?it/s]
Out[18]:
























In [19]:



     


X=df.drop('class',axis=1) #independent



     



















In [20]:



     


y=df[['class']]



     



















In [21]:



     


X.head()



     






















    
    
Out[21]:











  
    

      
      Preg
      Plas
      Pres
      skin
      test
      mass
      pedi
      age
    

  
  
    

      0
      6
      148
      72
      35
      0
      33.6
      0.627
      50
    

    

      1
      1
      85
      66
      29
      0
      26.6
      0.351
      31
    

    

      2
      8
      183
      64
      0
      0
      23.3
      0.672
      32
    

    

      3
      1
      89
      66
      23
      94
      28.1
      0.167
      21
    

    

      4
      0
      137
      40
      35
      168
      43.1
      2.288
      33
    

  



























In [22]:



     


y.head()



     






















    
    
Out[22]:











  
    

      
      class
    

  
  
    

      0
      1
    

    

      1
      0
    

    

      2
      1
    

    

      3
      0
    

    

      4
      1
    

  



























In [23]:



     


from sklearn.model_selection import train_test_split



     



















In [24]:



     


X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.2,random_state=1)



     



















In [25]:



     


X_train.shape



     






















    
    
Out[25]:








(614, 8)
























In [26]:



     


X_test.shape



     






















    
    
Out[26]:








(154, 8)
























In [27]:



     


y_train.shape



     






















    
    
Out[27]:








(614, 1)
























In [28]:



     


y_test.shape



     






















    
    
Out[28]:








(154, 1)
























In [29]:



     


from sklearn.naive_bayes import GaussianNB



     



















In [30]:



     


nb=GaussianNB()



     



















In [31]:



     


nb.fit(X_train,y_train)



     






















    
    
Out[31]:







GaussianNB()
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. 
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
GaussianNB()
























In [32]:



     


nb.pred=nb.predict(X_test)



     



















In [33]:



     


nb.pred



     






















    
    
Out[33]:








array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0,
       1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1,
       0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0,
       0, 0, 1, 1, 1, 1, 0, 0, 1, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 1,
       0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0,
       0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0,
       1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0],
      dtype=int64)
























In [34]:



     


from sklearn.metrics import accuracy_score
from sklearn.metrics import confusion_matrix
from sklearn.metrics import classification_report



     



















In [35]:



     


accuracy_score(y_test,nb.pred)*100



     






















    
    
Out[35]:








77.27272727272727
























In [36]:



     


from sklearn.linear_model import LogisticRegression



     



















In [37]:



     


lr=LogisticRegression()



     



















In [38]:



     


lr.fit(X_train,y_train)



     






















    
    
Out[38]:







LogisticRegression()
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On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
LogisticRegression()
























In [39]:



     


lr_pred=lr.predict(X_test)



     



















In [40]:



     


lr_pred



     






















    
    
Out[40]:








array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0,
       1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0,
       0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 0,
       0, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0,
       0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0,
       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0,
       0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0],
      dtype=int64)
























In [41]:



     


accuracy_score=(y_test,lr_pred)*100



     



















In [42]:



     


from sklearn.neighbors import KNeighborsClassifier



     



















In [43]:



     


knn=KNeighborsClassifier(n_neighbors=2)



     



















In [44]:



     


knn.fit(X_train,y_train)



     






















    
    
Out[44]:







KNeighborsClassifier(n_neighbors=2)
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. 
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
KNeighborsClassifier(n_neighbors=2)
























In [45]:



     


knn_pred=knn.predict(X_test)



     



















In [46]:



     


knn_pred



     






















    
    
Out[46]:








array([1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0,
       0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0,
       0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 1, 0,
       0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0,
       0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0,
       1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0,
       0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
      dtype=int64)
























In [47]:



     


accuracy_score=(y_test,knn_pred)*100



     



















In [48]:



     


cf_nb=confusion_matrix(y_test,nb.pred)
cf_nb



     






















    
    
Out[48]:








array([[85, 14],
       [21, 34]], dtype=int64)
























In [49]:



     


plt.figure(figsize=(10,5))
plt.title('Confusion_matrix',fontsize=20)
sns.heatmap(cf_nb,annot=True,fmt='g')



     






















    
    
Out[49]:








<Axes: title={'center': 'Confusion_matrix'}>










    
    































In [50]:



     


cf_lr=confusion_matrix(y_test,lr_pred)
cf_lr



     






















    
    
Out[50]:








array([[89, 10],
       [24, 31]], dtype=int64)
























In [51]:



     


cf_nb



     






















    
    
Out[51]:








array([[85, 14],
       [21, 34]], dtype=int64)
























In [52]:



     


plt.figure(figsize=(10,5))
plt.title('Confusion_matrix_LR',fontsize=20)
sns.heatmap(cf_lr,annot=True,fmt='g')



     






















    
    
Out[52]:








<Axes: title={'center': 'Confusion_matrix_LR'}>










    
    































In [53]:



     


df['class'].value_counts()



     






















    
    
Out[53]:








0    500
1    268
Name: class, dtype: int64